New Flow-based Heuristic for Search Algorithms Solving Multi-agent Path Finding

Švancara, Jiří; Surynek, Pavel

doi:10.5220/0006184504510458

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…Multi-robot motion planning approaches can be divided into centralized methods and Decentralized methods. Centralized methods contain optimization-based trajectory generation methods such as [1], [2], and heuristic search-based planning methods such as [3], [4]. Centralized methods have the advantage of completeness or probabilistic completeness.…”

Section: A Related Workmentioning

confidence: 99%

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

He¹,

Song²,

Sun³

2021

Preprint

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In this paper, a novel hybrid multi-robot motion planner that can be applied under non-communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multi-robot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multi-agent soft actor-critic algorithm under the centralized training with decentralized execution diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multi-group experimental results verify the effectiveness of the proposed motion planner.

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Section: A Related Workmentioning

confidence: 99%

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

He¹,

Song²,

Sun³

2021

Preprint

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“…Surynek [31] utilized the search approaches that examine systematically the search space. These algorithms need a well-defined heuristic function to be able to enhance the computational efficiency via altering the order in which the states are extended.…”

Section: Svancara Andmentioning

confidence: 99%

The Maximum Flow Problem

Abd-Alsabour¹

2020

International Journal of Engineering Research and Technology

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In many fields, there exist various crucial applications that can be handled as maximum flow problems. Examples are: electrical power, airline scheduling, communication networks, computer sciences, etc. This clarifies why various researchers have handled them (which are NP optimization problems and a type of network optimization problems) with the utilization of various approaches. The paper explores this as we tackled it by the utilization of two approaches dealing with different instances from various domains. Furthermore, the approaches based on the network flow are addressed. Additionally, the latest applications of the most well-known as well as the recent approaches tackling this crucial optimization problem are detailed. Further, the approaches for dealing with other optimization problems based on the maximum flow as well as the most recent real-world applications are highlighted.

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“…To provide optimality, centralized approaches operate over the composite space of all the agents. Examples include the increasing-cost tree search (Sharon, Stern, Goldenberg, & Felner, 2013), conflict-based search (Sharon, Stern, Felner, & Sturtevant, 2015), M* (Wagner & Choset, 2015), A* and its variants (Standley & Korf, 2011;Goldenberg, Felner, Stern, Sharon, Sturtevant, Holte, & Schaeffer, 2014;Svancara & Surynek, 2017), which seek to divide the agents into independent groups, avoid surplus nodes, dynamically change the dimensionality based on conflicts, or develop effective heuristics.…”

Section: Multi-agent Search Over Graphsmentioning

confidence: 99%

Cooperative, Dynamics-based, and Abstraction-Guided Multi-robot Motion Planning

Le¹,

Plaku²

2018

jair

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This paper presents an effective, cooperative, and probabilistically-complete multi-robot motion planner that enables each robot to move to a desired location while avoiding collisions with obstacles and other robots. The approach takes into account not only the geometric constraints arising from collision avoidance, but also the differential constraints imposed by the motion dynamics of each robot. This makes it possible to generate collision-free and dynamically-feasible trajectories that can be executed in the physical world.The salient aspect of the approach is the coupling of sampling-based motion planning to handle the complexity arising from the obstacles and robot dynamics with multi-agent search to find solutions over a suitable discrete abstraction. The discrete abstraction is obtained by constructing roadmaps to solve a relaxed problem that accounts for the obstacles but not the dynamics. Sampling-based motion planning expands a motion tree in the composite state space of all the robots by adding collision-free and dynamically-feasible trajectories as branches. Efficiency is obtained by using multi-agent search to find non-conflicting routes over the discrete abstraction which serve as heuristics to guide the motion-tree expansion. When little or no progress is made, the routes are penalized and the multi-agent search is invoked again to find alternative routes. This synergistic coupling makes it possible to effectively plan collision-free and dynamically-feasible motions that enable each robot to reach its goal. Experiments using vehicle models with nonlinear dynamics operating in complex environments, where cooperation among robots is required, show significant speedups over related work.

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New Flow-based Heuristic for Search Algorithms Solving Multi-agent Path Finding

Cited by 10 publications

References 10 publications

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

The Maximum Flow Problem

Cooperative, Dynamics-based, and Abstraction-Guided Multi-robot Motion Planning

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